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Archive for December 5th, 2013

 

Using Big Data to Improve Customer Metrics

Thursday, December 5th, 2013 by

Although Big Data projects can be helpful to many organizations, launching these initiatives using the wrong metrics can lead to poor practices. And even though most experts agree these endeavors aren’t a panacea, business decision-makers continue to hastily adopt strategies that increase challenges.

Using big data to improve customer metrics

Using Big Data to improve customer metrics

An Enterprise Apps Today report, which highlighted the mindset of customer analytics expert Larry Freed, noted that the gathered data by itself won’t necessarily provide firms with opportunities. Businesses must also embrace the right tools to take advantage of those digital resources.

Freed believes that the variety of information being collected will give organizations the biggest challenge when adopting Big Data endeavors because some of the information being gathered isn’t always accurate.

“If I am an anonymous visitor to a website on my tablet, my home PC, and my work PC, companies are not going to have the ability to recognize that I am the same person,” Freed said, according to Enterprise Apps Today. “Companies are gathering enormous amounts of digital data, but it can accelerate some bad practices.”

In other words, context is everything. Companies need to proactively establish parameters to ensure they understand prospective and existing customer behavior and have the tools needed to turn activity into useful information. Doing so, however, may require the development of new metrics.

More detail is needed
In the past, businesses would often use generic metrics that would put customers in one of three categories based on their response to whether or not they would recommend a company to other people, Freed asserted. The classifications — promoter, passive, or detractor — would then tell decision-makers how “loyal” clients are. During the past several years, however, executives have learned that these categories aren’t entirely accurate, which also means that the strategies built around this segregated system weren’t always effective.

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